Bivariate Analysis

Through bivariate analysis we try to analyze two variables simultaneously. As opposed to univariate analysis where we check the characteristics of a single variable, in bivariate analysis we try to determine if there is any relationship between two variables.

 

There are essentially 3 major scenarios that we will come across when we perform bivariate analysis

1. Both variables of interest are qualitative
2. One variable is qualitative and the other is quantitative
3. Both variables are quantitative

 

For the purpose of this exercise, we will explore few most popular techniques to perform bivariate analysis.

The following plots are not limited to the headings they are under. They are the options we have if we face a certain scenario.

Numerical Vs Numerical

• Scatterplot
• line chart
• heatmap for correlation
• joint plot

Numerical vs Categorical

• Bar chart
• violin plot
• Categorical boxplot
• swarm plot

Categorical vs categorical

• Bar chart
• Group Bar chart
• Point plot

Case Study (Suicide Rates Overview 1985 to 2016)

Our aim is to explore the data of suicide rates.

• Lets start using the above techniques on a practical dataset. We will be using data about suicide rates from 1985 to 2026.

source: https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016

1. Dataset Feature List
2. country
3. year
4. sex
5. age
6. suicides_no
7. population
8. suicides/100k pop
9. country-year
10. HDI for year
11. gdp_for_year
12. gdp_per_capita
13. generation

1.1 Loading Libraries

import warnings
warnings.filterwarnings('ignore')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style('darkgrid')

Import the dataset Master.csv

data = pd.read_csv('master.csv')
data.head()

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
0	Albania	1987	male	15-24 years	21	312900	6.71	Albania1987	NaN	2,156,624,900	796	Generation X
1	Albania	1987	male	35-54 years	16	308000	5.19	Albania1987	NaN	2,156,624,900	796	Silent
2	Albania	1987	female	15-24 years	14	289700	4.83	Albania1987	NaN	2,156,624,900	796	Generation X
3	Albania	1987	male	75+ years	1	21800	4.59	Albania1987	NaN	2,156,624,900	796	G.I. Generation
4	Albania	1987	male	25-34 years	9	274300	3.28	Albania1987	NaN	2,156,624,900	796	Boomers

# Check the data describe
data.describe()

	year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
count	27820.000000	27820.000000	2.782000e+04	27820.000000	8364.000000	27820.000000
mean	2001.258375	242.574407	1.844794e+06	12.816097	0.776601	16866.464414
std	8.469055	902.047917	3.911779e+06	18.961511	0.093367	18887.576472
min	1985.000000	0.000000	2.780000e+02	0.000000	0.483000	251.000000
25%	1995.000000	3.000000	9.749850e+04	0.920000	0.713000	3447.000000
50%	2002.000000	25.000000	4.301500e+05	5.990000	0.779000	9372.000000
75%	2008.000000	131.000000	1.486143e+06	16.620000	0.855000	24874.000000
max	2016.000000	22338.000000	4.380521e+07	224.970000	0.944000	126352.000000

#check the number of Columns
data.columns

Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',
       'suicides/100k pop', 'country-year', 'HDI for year',
       ' gdp_for_year ($) ', 'gdp_per_capita ($)', 'generation'],
      dtype='object')

#Check the shape of the data
data.shape

(27820, 12)

#count the data types
data.dtypes

country                object
year                    int64
sex                    object
age                    object
suicides_no             int64
population              int64
suicides/100k pop     float64
country-year           object
HDI for year          float64
 gdp_for_year ($)      object
gdp_per_capita ($)      int64
generation             object
dtype: object

data.dtypes.value_counts()

object     6
int64      4
float64    2
dtype: int64

#check the dataset info
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27820 entries, 0 to 27819
Data columns (total 12 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   country             27820 non-null  object 
 1   year                27820 non-null  int64  
 2   sex                 27820 non-null  object 
 3   age                 27820 non-null  object 
 4   suicides_no         27820 non-null  int64  
 5   population          27820 non-null  int64  
 6   suicides/100k pop   27820 non-null  float64
 7   country-year        27820 non-null  object 
 8   HDI for year        8364 non-null   float64
 9    gdp_for_year ($)   27820 non-null  object 
 10  gdp_per_capita ($)  27820 non-null  int64  
 11  generation          27820 non-null  object 
dtypes: float64(2), int64(4), object(6)
memory usage: 2.5+ MB

#Chcecking the missing values
data.isnull().sum().sort_values(ascending = False)

HDI for year          19456
country                   0
year                      0
sex                       0
age                       0
suicides_no               0
population                0
suicides/100k pop         0
country-year              0
 gdp_for_year ($)         0
gdp_per_capita ($)        0
generation                0
dtype: int64

data.shape[0]

27820

#write a function that take dataframe and give the total and percentage missing values
def missing_data(df):
    total = df.isnull().sum().sort_values(ascending = False)
    count = df.shape[0]
    percent = round(total / count,2) * 100
    value = pd.concat([total,percent],axis = 1,keys=['Total','Percent'])
    return value

result = missing_data(data)
result

	Total	Percent
HDI for year	19456	70.0
country	0	0.0
year	0	0.0
sex	0	0.0
age	0	0.0
suicides_no	0	0.0
population	0	0.0
suicides/100k pop	0	0.0
country-year	0	0.0
gdp_for_year ($)	0	0.0
gdp_per_capita ($)	0	0.0
generation	0	0.0

#descriptive stats of continuous columns
data.columns

Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',
       'suicides/100k pop', 'country-year', 'HDI for year',
       ' gdp_for_year ($) ', 'gdp_per_capita ($)', 'generation'],
      dtype='object')

data[['suicides_no','population','suicides/100k pop','HDI for year',' gdp_for_year ($) ','gdp_per_capita ($)']].describe()

	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
count	27820.000000	2.782000e+04	27820.000000	8364.000000	27820.000000
mean	242.574407	1.844794e+06	12.816097	0.776601	16866.464414
std	902.047917	3.911779e+06	18.961511	0.093367	18887.576472
min	0.000000	2.780000e+02	0.000000	0.483000	251.000000
25%	3.000000	9.749850e+04	0.920000	0.713000	3447.000000
50%	25.000000	4.301500e+05	5.990000	0.779000	9372.000000
75%	131.000000	1.486143e+06	16.620000	0.855000	24874.000000
max	22338.000000	4.380521e+07	224.970000	0.944000	126352.000000

1.10 Frequency table for age

# crosstab group by a series by age
pd.crosstab(index = data['age'], columns='count')

col_0	count
age
15-24 years	4642
25-34 years	4642
35-54 years	4642
5-14 years	4610
55-74 years	4642
75+ years	4642

1.11 check number of suicides by country

#check the highest number of suicides

data.groupby('country')['suicides_no'].sum().reset_index().sort_values('suicides_no',ascending = False).head(10).plot(x = 'country', y = 'suicides_no',kind='bar',figsize=(15,5))

<AxesSubplot:xlabel='country'>

• Russia has the highest number of suicides followed by the US and Japan
• Russia, the US and Japan have exceptionally high counts of suicides compared to the others in the lot

#Check the lowest number of suicides
data.groupby('country')['suicides_no'].sum().reset_index().sort_values(['suicides_no'],ascending = True).head(10).plot(x = 'country', y = 'suicides_no',kind='bar',figsize = (15,5))

<AxesSubplot:xlabel='country'>

1.13 Barplot for the number of suicides vs Age

plt.figure(figsize=(15,5))#increase the screen size to (15,5)
sns.barplot(x = 'age', y ='suicides_no',data=data)

<AxesSubplot:xlabel='age', ylabel='suicides_no'>

Barplot Number of suicides vs sex

plt.figure(figsize = (8,4))
sns.barplot(x='sex', y= 'suicides_no',data = data)

<AxesSubplot:xlabel='sex', ylabel='suicides_no'>

1.15 Bar plot Number of Suicides Vs generation

plt.figure(figsize = (15,5))
sns.barplot(x = 'generation', y ='suicides_no', data = data)

<AxesSubplot:xlabel='generation', ylabel='suicides_no'>

• Suicides are the highest among the Boomers and lowest among Generation Z

1.15 Scatter plot Number of Suicides Vs population

plt.figure(figsize=(15,5))
sns.scatterplot(x = "population", y = 'suicides_no', data = data,hue ='sex'),

(<AxesSubplot:xlabel='population', ylabel='suicides_no'>,)

figure = plt.figure(figsize=(50,15))

ax = sns.regplot(x='population',y='suicides_no', data=data ) # regression plot - scatter plot with a regression line

#Here we plotting a line plot.
sns.lineplot(x='population',y='suicides_no', data=data.head(10) )

<AxesSubplot:xlabel='population', ylabel='suicides_no'>

Scatter plot Number of Suicides/100k Population Vs GDP Per Capita

data.columns

Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',
       'suicides/100k pop', 'country-year', 'HDI for year',
       ' gdp_for_year ($) ', 'gdp_per_capita ($)', 'generation'],
      dtype='object')

plt.figure(figsize = (15,5))
sns.scatterplot(x ='gdp_per_capita ($)', y ='suicides/100k pop',data = data )

<AxesSubplot:xlabel='gdp_per_capita ($)', ylabel='suicides/100k pop'>

plt.figure(figsize = (50,15))
sns.regplot(x ='gdp_per_capita ($)', y ='suicides/100k pop',data = data )

<AxesSubplot:xlabel='gdp_per_capita ($)', ylabel='suicides/100k pop'>

• Looks like higher suicide rates are a bit more prevalent in countries with lower GDP
• However, it doesn't look like there any significant correlation between the two

Correlation among pairs of continuous variables

data.corr()

	year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
year	1.000000	-0.004546	0.008850	-0.039037	0.366786	0.339134
suicides_no	-0.004546	1.000000	0.616162	0.306604	0.151399	0.061330
population	0.008850	0.616162	1.000000	0.008285	0.102943	0.081510
suicides/100k pop	-0.039037	0.306604	0.008285	1.000000	0.074279	0.001785
HDI for year	0.366786	0.151399	0.102943	0.074279	1.000000	0.771228
gdp_per_capita ($)	0.339134	0.061330	0.081510	0.001785	0.771228	1.000000

plt.figure(figsize = (10,10))
sns.heatmap(data.corr(),annot = True,fmt='.2f',linewidths=0.5,center = 1)

<AxesSubplot:>

• Instead of doing a simple df.corr(), we can use the heatmap when there are large number of variables
• The color helps with picking out the most correlated easily
• In the above case, we don't have a lot of variables the plot is just for demonstration
• The darker the color higher the correlation
• None of the attributes seem have correlation of real significance
• Some obvious correlations are that in a larger population, it is very likely that the number of suicides will be more
• Human Development Index - gdp per capita is the only pair with the most correlation

1.17.1 Bar plot To check Number of suicides by sex and age (three variables used to generate a single plot

• This is an example of Numerical and 2 Categorical variables

plt.figure(figsize = (15,5))
sns.barplot(x= 'sex',y = 'suicides_no', data = data,hue ='age')

<AxesSubplot:xlabel='sex', ylabel='suicides_no'>

• 1.17.2 Bar plot To check Number of suicides by sex and Generation(three variables used to generate a single plot)

plt.figure(figsize=(15,5))
sns.barplot(data=data,x='sex',y='suicides_no',hue='generation')
plt.show()

• Suicides in males apart from being higher, has a slight variation in distribution across generations compared to the suicides of females across generation
• In case of males, Generation X has higher suicide numbers compared to G.I Generation but in case of females, it is the opposite

1.18 Checking the No.of suicides: Country Vs Sex

data.head()
data1 = data.groupby("country")['suicides_no'].sum().reset_index().sort_values("suicides_no",ascending = False)
plt.figure(figsize = (15,5))
sns.barplot(x= "country", y = "suicides_no",data=data1.head(10))

<AxesSubplot:xlabel='country', ylabel='suicides_no'>

#sort values by country,sex vs suicides_no
data2 = data.groupby(["country","sex"])['suicides_no'].sum().reset_index().sort_values("suicides_no",ascending = False)
plt.figure(figsize = (15,10))
sns.barplot(x= "country", y = "suicides_no",data=data2.head(10),hue ='sex')

<AxesSubplot:xlabel='country', ylabel='suicides_no'>

• Japan has a higher proportion of female suicides compared to the countries with overall suicide rates even more high

Average number of suicides across each generation for a given gender along with the confidence intervals - Point Plot

plt.figure(figsize = (15,10))
sns.pointplot(x="generation", y="suicides_no",  data=data)

<AxesSubplot:xlabel='generation', ylabel='suicides_no'>

• The graph tells us the average suicides along with the confidence intervals
• Suicides among females in general don't seem to be fluctuating a lot
• Average suicides of Gen-Z are almost equally distributed across genders

Distribution of population across each generation - Violin plot

data.columns

Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',
       'suicides/100k pop', 'country-year', 'HDI for year',
       ' gdp_for_year ($) ', 'gdp_per_capita ($)', 'generation'],
      dtype='object')

plt.figure(figsize = (15,5))
sns.violinplot(x = data['generation'], y = data['population'], data = data)

<AxesSubplot:xlabel='generation', ylabel='population'>

• The plot is similar to a box plot but here, we get a density function
• Distribution of population across every generation is highly skewed
• Potential of lot of outliers
• Try it yourself - Go ahead and check if there really are a lot of outliers in populations across each generation

plt.figure(figsize = (15,5))
sns.boxplot(x = data['generation'], y = data['population'], data = data)

<AxesSubplot:xlabel='generation', ylabel='population'>

Checking trends with Temporal Data

• Temporal data is simply data that represents a state in time, such as the land-use patterns of Hong Kong in 1990,
• or total rainfall in Honolulu on July 1, 2009. Temporal data is collected to analyze weather patterns and other environmental variables, monitor traffic conditions, study demographic trends, and so on. This data comes from many sources ranging from manual data entry to data collected using observational sensors or generated from simulation models.

Checking pattern using Trend plot (1985-2015) suides Rate Vs Years

data[["suicides_no","year"]]
#data.columns

	suicides_no	year
0	21	1987
1	16	1987
2	14	1987
3	1	1987
4	9	1987
...	...	...
27815	107	2014
27816	9	2014
27817	60	2014
27818	44	2014
27819	21	2014

27820 rows × 2 columns

year_sui = data[["suicides_no","year"]].groupby('year').sum()
year_sui.head()

	suicides_no
year
1985	116063
1986	120670
1987	126842
1988	121026
1989	160244

year_sui.plot(figsize=(10,6))

<AxesSubplot:xlabel='year'>

Checking pattern using Trend plot (1985-2015) Population Vs Years

year_pop = data[["year","population"]].groupby('year').sum()
year_pop.head()

	population
year
1985	1008600086
1986	1029909613
1987	1095029726
1988	1054094424
1989	1225514347

year_pop.plot(figsize = (10,6))

<AxesSubplot:xlabel='year'>

Pandas Profiling with IPL data

• Generates profile reports from a pandas DataFrame.

• pandas_profiling extends the pandas DataFrame with df.profile_report() for quick data analysis.

• Link - https://pypi.org/project/pandas-profiling/

df = pd.read_csv('match_data.csv')

df.head()

	id	season	city	date	team1	team2	toss_winner	toss_decision	winner	eliminator	...	overs	player_of_match	venue	umpire1	umpire2	umpire3	first_bat_team	first_bowl_team	first_bat_score	second_bat_score
0	419164	2010	Mumbai	2010-04-24	Royal Challengers Bangalore	Deccan Chargers	Deccan Chargers	bat	Royal Challengers Bangalore	NaN	...	20	A Kumble	Dr DY Patil Sports Academy	RE Koertzen	SJA Taufel	NaN	Deccan Chargers	Royal Challengers Bangalore	82.0	86.0
1	419131	2010	Delhi	2010-03-29	Delhi Daredevils	Kolkata Knight Riders	Delhi Daredevils	bat	Delhi Daredevils	NaN	...	20	DA Warner	Feroz Shah Kotla	SS Hazare	SJA Taufel	NaN	Delhi Daredevils	Kolkata Knight Riders	177.0	137.0
2	336021	2008	Mumbai	2008-05-16	Mumbai Indians	Kolkata Knight Riders	Mumbai Indians	field	Mumbai Indians	NaN	...	20	SM Pollock	Wankhede Stadium	BR Doctrove	DJ Harper	NaN	Kolkata Knight Riders	Mumbai Indians	67.0	68.0
3	980931	2016	Pune	2016-04-22	Rising Pune Supergiants	Royal Challengers Bangalore	Rising Pune Supergiants	field	Royal Challengers Bangalore	NaN	...	20	AB de Villiers	Maharashtra Cricket Association Stadium	CB Gaffaney	VK Sharma	NaN	Royal Challengers Bangalore	Rising Pune Supergiants	185.0	172.0
4	419163	2010	Mumbai	2010-04-22	Chennai Super Kings	Deccan Chargers	Chennai Super Kings	bat	Chennai Super Kings	NaN	...	20	DE Bollinger	Dr DY Patil Sports Academy	BR Doctrove	RB Tiffin	NaN	Chennai Super Kings	Deccan Chargers	142.0	104.0

5 rows × 24 columns

It has many useful functionalities but the best one is to generate an EDA report as given below.

#Installation step 
#!pip install pandas-profiling
#or 
import sys
!{sys.executable} -m pip install pandas-profiling

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Requirement already satisfied: certifi>=2017.4.17 in c:\users\chonc\anaconda3\envs\panda_playground\lib\site-packages (from requests>=2.24.0->pandas-profiling) (2021.5.30)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\chonc\anaconda3\envs\panda_playground\lib\site-packages (from requests>=2.24.0->pandas-profiling) (1.26.7)
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Requirement already satisfied: colorama in c:\users\chonc\anaconda3\envs\panda_playground\lib\site-packages (from tqdm>=4.48.2->pandas-profiling) (0.4.4)
Requirement already satisfied: PyWavelets in c:\users\chonc\anaconda3\envs\panda_playground\lib\site-packages (from imagehash->visions[type_image_path]==0.7.4->pandas-profiling) (1.1.1)

#import pandas_profiling
import pandas_profiling 

#Getting the pandas profiling report 
pandas_profiling.ProfileReport(df)

#Getting an html file as output here 
pandas_profiling.ProfileReport(df).to_file("pandas_profiling.html")

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